DATA MINING AND BUSINESS INTELLIGENCE
- Course
- MISY542 - DATA MINING AND BUSINESS INTELLIGENCE
- Department
- Master of Management Information Systems - English - Master
- Course Type
- Course
- Status
- Required
- Language
- English
- Credit
- 3
- ECTS
- 0
- T+P+L
- 3 + 0 + 0
- Course Coordinator(s)
- -
- Prerequisite
- -
- Keywords
- -
Course Description
-
DATA MINING AND BUSINESS INTELLIGENCE
Evaluation Tools (Active Term)
No evaluation items have been defined.
Course outcomes
No course outcomes have been defined yet.
Course Syllabus
| Week | Topic |
|---|---|
| Week 1 | Introduction |
| Week 2 | An Overview of Business Intelligence |
| Week 3 | Data Warehousing |
| Week 4 | Business Reporting, Visual Analytics, and Business, Performance Management |
| Week 5 | Introduction to Data Mining |
| Week 6 | Data Mining for Business Intelligence |
| Week 7 | Data in Data Mining |
| Week 8 | Midterm Exams |
| Week 9 | Midterm Exams |
| Week 10 | Project Presentations |
| Week 11 | Basic Data Classification |
| Week 12 | Cluster Analysis: Basic Concepts and Algorithms |
| Week 13 | Data Mining Processes |
| Week 14 | Data Mining Applications |
| Week 15 | Project Presentations |
Reference Books & Course Materials
- 01 Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner, 2nd Edition, Galit Shmueli; Nitin R. Patel; Peter C. Bruce
- 02 Business Intelligence: A Managerial Perspective on Analytics, 3/E, Ramesh Sharda, Dursun Delen, Efraim Turban
- 03 Data Mining Techniques and Applications , 1st Ed., Hongbo Du, Cengage Learning
- 04 Introduction to Data Mining: Pearson New International Edition, 1st Ed., Pang-Ning Tan; Michael Steinbach; Vipin Kumar, Pearson.
Learning Outcomes
No learning outcomes have been defined.
Program Outcomes
- Demonstrate a thorough understanding of the theories, frameworks, and models in order to assess and comprehend the state of the art in data science.
- Review the literature and apply data science theories and methodology in new research and experiments.
- Analyze datasets using supervised and unsupervised machine learning techniques.
- Design, develop and test statistics and informatics software systems for data management, analysis and problem solving.
- Conceptualize and develop efficient visuals for a range of data types and analytical tasks, and carry out independent research on a range of theoretical and applied subjects in visualization and visual analytics.
- Obtain a high level of proficiency in communication, problem solving, research or project-related activities and function effectively as a team member or a leader to accomplish a common goal in a multidisciplinary team.
- Develop and implement optimal solutions to overcome challenges associated with managing large datasets by utilizing parallel methods, cloud computing, and non-relational data storage and retrieval (NoSQL).
- Demonstrate an understanding of the interdisciplinary of data, information, and communications, as well as the ability to evaluate the leading research methods for data collection and analysis.
- Demonstrate a deep understanding of the ethical issues surrounding the use of data and apply ethical decision making in real-world data-related applications.
- Demonstrate capability of analyzing, synthesizing, and evaluating knowledge from a wide range of fields and be capable of lifelong self-directed learning.
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